Three-dimensional Variational Data Assimilation Scheme Research Articles

Impact Study of FY-3B MWRI Data Assimilation in WRFDA

In the first attempt to configure the Fengyun-3B satellite’s Microwave Radiation Imager (MWRI) radiance data in the Weather Research Forecast (WRF) model’s Data Assimilation system (WRFDA), the impact of MWRI data assimilation on the analysis and forecast of Typhoon Son-Tinh in 2012 was evaluated with WRFDA’s three-dimensional variational (3DVAR) data-assimilation scheme. Compared to a benchmark experiment with no MWRI data, assimilating MWRI radiances improved the analyses of typhoon central sea level pressure (CSLP), warm core structure, and wind speed. Moreover, verified with European Center for Medium-Range Weather Forecasts (ECMWF) analysis data, significant improvements in model variable forecast, such as geopotential height and specific humidity, were produced. Substantial error reductions in track, CSLP, and maximum-wind-speed forecasts with MWRI assimilation was also obtained from analysis time to 48 h forecast.

Assimilation of coastal and open sea biogeochemical data to improve phytoplankton simulation in the Mediterranean Sea

The high spatial and temporal variability of biogeochemical features, induced by local dynamics and terrestrial and atmospheric inputs in shelf seas, are challenging issues for the implementation of data assimilation in these areas. The objective of this study is to integrate satellite ocean-colour observations with a coupled physical-biogeochemical model in order to improve the spatiotemporal descriptions of chlorophyll and other biogeochemical variables in the Mediterranean shelf seas. We adopted a specifically developed three-dimensional variational data assimilation scheme for the assimilation of satellite chlorophyll data. The non-homogeneous vertical component, the non-uniform and direction-dependent horizontal component of the background error covariance are the key features of the upgraded three-dimensional variational data assimilation scheme for shelf seas. The application of the new assimilation scheme significantly improves chlorophyll estimates in shelf seas, particularly in the representation of their spatial and temporal variability. Based on these results, we provide an estimate of the annual primary production of the Mediterranean basin.

A THTEE-DIMENSIONAL VARIATIONAL ASSIMILATION SCHEME FOR SATELLITE AOD

Abstract. A three-dimensional variational data assimilation scheme is designed for satellite AOD based on the IMPROVE (Interagency Monitoring of Protected Visual Environments) equation. The observation operator that simulates AOD from the control variables is established by the IMPROVE equation. All of the 16 control variables in the assimilation scheme are the mass concentrations of aerosol species from the Model for Simulation Aerosol Interactions and Chemistry scheme, so as to take advantage of this scheme in providing comprehensive analyses of species concentrations and size distributions as well as be calculating efficiently. The assimilation scheme can save computational resources as the IMPROVE equation is a quadratic equation. A single-point observation experiment shows that the information from the single-point AOD is effectively spread horizontally and vertically.

Numerical Effects of the Gaussian Recursive Filters in Solving Linear Systems in the 3Dvar Case Study

AbstractIn many applications, the Gaussian convolution is approximately computed by means of recursive filters, with a significant improvement of computational efficiency. We are interested in theoretical and numerical issues related to such an use of recursive filters in a three-dimensional variational data assimilation (3Dvar) scheme as it appears in the software OceanVar. In that context, the main numerical problem consists in solving large linear systems with high efficiency, so that an iterative solver, namely the conjugate gradient method, is equipped with a recursive filter in order to compute matrix-vector multiplications that in fact are Gaussian convolutions. Here we present an error analysis that gives effective bounds for the perturbation on the solution of such linear systems, when is computed by means of recursive filters. We first prove that such a solution can be seen as the exact solution of a perturbed linear system. Then we study the related perturbation on the solution and we demonstrate that it can be bounded in terms of the difference between the two linear operators associated to the Gaussian convolution and the recursive filter, respectively. Moreover, we show through numerical experiments that the error on the solution, which exhibits a kind of edge effect, i.e. most of the error is localized in the first and last few entries of the computed solution, is due to the structure of the difference of the two linear operators.

Observing System Experiments in a 3DVAR Data Assimilation System at CPTEC/INPE

Abstract The Center for Weather Forecast and Climate Studies [Centro de Previsão e Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] has recently operationally implemented a three-dimensional variational data assimilation (3DVAR) scheme based on the Gridpoint Statistical Interpolation analysis system (GSI). Implementation of the GSI system within the atmospheric global circulation model from CPTEC/INPE (AGCM-CPTEC/INPE) is hereafter referred to as the Global 3DVAR (G3DVAR) system. The results of an observing system experiment (OSE) measuring the impacts of radiosonde, satellite radiance, and GPS radio occultation (RO) data on the new G3DVAR system are presented here. The observational impact of each of these platforms was evaluated by measuring the degradation of the geopotential height anomaly correlation and the amplification of the RMSE of the wind. Losing the radiosonde, GPS RO, and satellite radiance data in the OSE resulted in negative impacts on the geopotential height anomaly correlations globally. Nevertheless, the strongest impacts were found over the Southern Hemisphere and South America when satellite radiance data were withheld from the data assimilation system.

Data assimilation of Argo profiles in a northwestern Pacific model

Abstract. Based on a novel estimation of background-error covariances for assimilating Argo profiles, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme was developed for the northwestern Pacific Ocean model (NwPM) for potential use in operational predictions and maritime safety applications. Temperature and salinity data extracted from Argo profiles from January to December 2010 were assimilated into the NwPM. The results show that the average daily temperature (salinity) root mean square error (RMSE) decreased from 0.99 °C (0.10 psu) to 0.62 °C (0.07 psu) in assimilation experiments throughout the northwestern Pacific, which represents a 37.2 % (27.6 %) reduction in the error. The temperature (salinity) RMSE decreased by ∼ 0.60 °C ( ∼ 0.05 psu) for the upper 900 m (1000 m). Sea level, temperature and salinity were in better agreement with in situ and satellite datasets after data assimilation than before. In addition, a 1-month experiment with daily analysis cycles and 5-day forecasts explored the performance of the system in an operational configuration. The results highlighted the positive impact of the 3DVAR initialization at all forecast ranges compared to the non-assimilative experiment. Therefore, the 3DVAR scheme proposed here, coupled to ROMS, shows a good predictive performance and can be used as an assimilation scheme for operational forecasting.

Impact of high-frequency observations on fog forecasting: a case study of OSSE

Fog that refers to the concentration of ice or water droplets in the near surface air is an important short-time meteorological phenomenon. As the measure of visibility of air environment, it directly affects societal economic activities and daily lives. As more and more high-frequency observations (observations with short time intervals) become available, understanding how to make full use of such observed data to improve fog forecasting is an important and urgent research topic. Based on the Weather Research and Forecasting (WRF) Model and an observation simulation system experiment (OSSE) framework, this study explores a modified three-dimensional variational (3D-Var) data assimilation (DA) scheme to address the utilization of high-frequency observations on fog forecasting. In the modified 3D-Var scheme, the large-scale analysis constraint (LSAC) method is employed to the WRF 3D-Var. A dense fog event, which occurred in the North of China in 2007, is selected for the case study. Experimental results show that coherently combining high-frequency observational information with large-scale analysis information enables to significantly improve the 3D-Var analyses and the initialized model forecasts of fog coverage, especially over areas with coarse observations. The modified scheme is therefore promising for improving the routine forecasting of coastal sea fog. The optimal DA interval for fog forecasting is also discussed in this study.

AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system

A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed within the Weather Research and Forecasting (WRF) model's data assimilation (WRFDA) system. The four essential elements are: (1) extending the community radiative transform model's (CRTM) interface to include hydrometeor profiles; (2) using total water Qt as the moisture control variable; (3) using a warm-rain physics scheme for partitioning the Qt increment into individual increments of water vapour, cloud liquid water and rain; and (4) adopting a symmetric observation error model for all-sky radiance assimilation.Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or all-sky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012) was assessed through analysis/forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR) data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC) core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP), warm-core structure and cloud distribution. Substantial (>20 %) error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the all-sky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF) analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.

Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction

Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW) sounder and an infrared (IR) sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV) compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF) model through three-dimensional variational (3D-var) data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1) clear AIRS single field-of-view (SFOV); (2) retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP); (3) all SFOV soundings within AMSU FOVs must be clear; and (4) SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison. Various atmospheric state variables at different pressure levels are used to assess the impact from assimilating these different data by comparing them with European Centre for Medium Range Weather Forecast (ECMWF) reanalysis data. Results indicate assimilation of SUP soundings improve the mid and upper troposphere, whereas assimilation of SFOV soundings has positive impact on the lower troposphere. Two additional assimilation experiments are carried out to determine the combination of SUP and SFOV soundings that will provide the best performance throughout the troposphere. The results indicate that optimal combination is to assimilate clear-sky matched IR retrievals with non-matched MW soundings.

Assimilation of zenith total delays in the AROME France convective scale model: a recent assessment

The impact of assimilating GPS zenith total delays (ZTD) in the convective scale model AROME is assessed over a 1-month period in summer 2013. The experimental set-up is similar to the current operational usage at Météo-France where the observing system has been expanded in July 2013 in a three-dimensional variational (3D-Var) data assimilation scheme with a 3-hour cycling. Three experiments are performed. In a baseline experiment the GPS ZTD provided through the E-GVAP programme are withdrawn from the observing system (NOGPS). In a second experiment, GPS ZTD from E-GVAP are included in the observing system, representing the operational configuration at Météo-France (EGVAP). The last experiment is similar to EGVAP but new ZTD observations processed by the University of Luxembourg are also assimilated on top of all other observations (UL01). In the first stage, it has been verified through a systematic comparison with model counterparts that the quality of ZTD data processed by the University of Luxembourg is similar to the one provided by other analysis centres from the E-GVAP programme. After a number of quality controls, it has been possible to assimilate around 90 additional observations on top of around 600 stations from E-GVAP every 3 hours. Despite the small fraction of observations assimilated in AROME that ZTD represent (<2%), it is shown that they systematically improve the atmospheric humidity short-range forecasts by a comparison with other observing systems informative about water vapour (radiosoundings, satellite radiances, surface networks) even though it is by small amounts. When examining objective precipitation scores over France, the improvement brought by the UL01 stations on top of E-GVAP is systematic for all daily precipitation thresholds. Examination of several case studies reveals the ability of the ZTD observations to modify the intensity and location of precipitating areas in accordance with previous studies. The addition of ZTD from UL01 is also found to be beneficial, by improving rainfall patterns. Planned improvements to the AROME forecasting and assimilation systems with higher horizontal resolution and hourly cycling of 3D-Var assimilation will be of benefit to ZTD observations.

Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part II: The Regional System

Abstract The modifications to the data assimilation component of the Regional Deterministic Prediction System (RDPS) implemented at Environment Canada operations during the fall of 2014 are described. The main change is the replacement of the limited-area four-dimensional variational data assimilation (4DVar) algorithm for the limited-area analysis and the associated three-dimensional variational data assimilation (3DVar) scheme for the synchronous global driver analysis by the four-dimensional ensemble–variational data assimilation (4DEnVar) scheme presented in the first part of this study. It is shown that a 4DEnVar scheme using global background-error covariances can provide RDPS forecasts that are slightly improved compared to the previous operational approach, particularly during the first 24 h of the forecasts and in the summertime convective regime. Further forecast improvements were also made possible by upgrades in the assimilated observational data and by introducing the improved global analysis presented in the first part of this study in the RDPS intermittent cycling strategy. The computational savings brought by the 4DEnVar approach are also discussed.

An Impact Assessment of GPS Radio Occultation Data on Prediction of a Rapidly Developing Cyclone over the Southern Ocean*

Abstract The impact of global positioning system (GPS) radio occultation (RO) data on an intense synoptic-scale storm that occurred over the Southern Ocean in December 2007 is evaluated, and a synoptic explanation of the assessed impact is offered. The impact is assessed by using the three-dimensional variational data assimilation scheme (3DVAR) of the Weather Research and Forecasting (WRF) Model Data Assimilation system (WRFDA), and by comparing two experiments: one with and the other without assimilating the refractivity data from four different RO missions. Verifications indicate significant positive impacts of the RO data in various measures and parameters as well as in the track and intensity of the Antarctic cyclone. The analysis of the atmospheric processes underlying the impact shows that the assimilation of the RO data yields substantial improvements in the large-scale circulations that in turn control the development of the Antarctic storm. For instance, the RO data enhanced the strength of a 500-hPa trough over the Southern Ocean and prevented the katabatic flow near the coast of East Antarctica from an overintensification. This greatly influenced two low pressure systems of a comparable intensity, which later merged together and evolved into the major storm. The dominance of one low over the other in the merger dramatically changed the track, intensity, and structure of the merged storm. The assimilation of GPS RO data swapped the dominant low, leading to a remarkable improvement in the subsequent storm’s prediction.

Evaluating a mesoscale atmosphere model and a satellite-based algorithm in estimating extreme rainfall events in northwestern Turkey

Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the western Black Sea region of Turkey. Precipitation derived from WRF model with and without the three-dimensional variational (3DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (5 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and to some extent the spatial distribution and magnitude of the heavy rainfall events, whereas MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of the 3DVAR scheme. Direct impact of data assimilation on WRF precipitation reached up to 12% and at some points there is a quantitative match for heavy rainfall events, which are critical for hydrological forecasts.

An examination of a multi-scale three-dimensional variational data assimilation scheme in the Kuroshio Extension using the naval coastal ocean model

The implementation of a multi-scale three dimensional variational (MS3DVAR) data assimilation scheme for use with the Navy Coastal Ocean Model (NCOM) in the Kuroshio Extension western boundary current region is presented here. This work leverages on Li et al. (2013), who initially developed this method. MS3DVAR data assimilation allows for the effective assimilation of both spatially coarse and dense collections of observations. Traditional 3DVAR produces an inherent filtering of dynamical features smaller than the decorrelation length. The MS3DVAR allows for a scale selective background error covariance capable of handling a wider range of ocean scales. Here the MS3DVAR is examined in an energetic coastal regime using simulated and real observations. The results show that the MS3DVAR reduces analysis errors when compared to a traditional 3DVAR scheme. Forecast errors appear to be similar for both systems and are most likely due to the coarse resolution of the surface forceing being applied.

Analysis and numerical study of a hybrid BGM-3DVAR data assimilation scheme using satellite radiance data for heavy rain forecasts

A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm waterlogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode (BGM)- three-dimensional variational (3DVAR) Data Assimilation (DA) scheme is designed on running the Advanced Research WRF (ARW WRF) model using the Advanced Microwave Sounder Unit A (AMSU-A) satellite radiance data. Results show that: the BGM ensemble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM- 3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clusters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.

Evaluation of the Global Ocean Data Assimilation System at INCOIS: The Tropical Indian Ocean

A new version of NCEP’s Global Ocean Data Assimilation System (GODAS), which is based on the Geophysical Fluid Dynamics Laboratory (GFDL) Modular ocean Model version 4.0 (MOM4.0) and a three-dimensional variational (3D-VAR) data assimilation scheme, was configured and operationalized at Indian National Centre for Ocean Information Services (INCOIS). The primary objective of the GODAS at INCOIS (INCOIS–GODAS) is to provide an accurate estimate of the ocean state, which will be used to initialize a coupled model for the seasonal monsoon forecast and also to understand the variability of the ocean at different time scales. In this paper, we assess the quality of ocean analyses in the Tropical Indian Ocean (TIO) obtained from the operational INCOIS–GODAS. In addition to this, we examined the sensitivity of INCOIS–GODAS to different momentum forcing and to the assimilation of temperature and synthetic salinity based on the experiments carried out with different wind products: NCEP2 and QuikSCAT and a free run respectively. The present study reveals that the model with assimilation simulates most of the observed features of temperature, SSHA and currents with reasonably good accuracy in the TIO at both intra-seasonal and inter-annual time scales. The analysis further shows that there was a considerable improvement in the ocean current field, when the model was forced with QuikSCAT winds.

A theoretical study of the multigrid three-dimensional variational data assimilation scheme using a simple bilinear interpolation algorithm

In order to solve the so-called “bull-eye” problem caused by using a simple bilinear interpolation as an observational mapping operator in the cost function in the multigrid three-dimensional variational (3DVAR) data assimilation scheme, a smoothing term, equivalent to a penalty term, is introduced into the cost function to serve as a means of troubleshooting. A theoretical analysis is first performed to figure out what on earth results in the issue of “bull-eye”, and then the meaning of such smoothing term is elucidated and the uniqueness of solution of the multigrid 3DVAR with the smoothing term added is discussed through the theoretical deduction for one-dimensional (1D) case, and two idealized data assimilation experiments (one- and two-dimensional (2D) cases). By exploring the relationship between the smoothing term and the recursive filter theoretically and practically, it is revealed why satisfied analysis results can be achieved by using such proposed solution for the issue of the multigrid 3DVAR.

A 3DVAR-Based Dynamical Initialization Scheme for Tropical Cyclone Predictions*

Abstract A combined tropical cyclone dynamic initialization–three-dimensional variational data assimilation scheme (TCDI–3DVAR) is proposed. The specific procedure for the new initialization scheme is described as follows. First, a first-guess vortex field derived from a global analysis will be spun up in a full-physics mesoscale regional model in a quiescent environment. During the spinup period, the weak vortex is forced toward the observed central minimum sea level pressure (MSLP). The so-generated balanced TC vortex with realistic MSLP and a warm core is then merged into the environmental field and used in the subsequent 3DVAR data assimilation. The observation system simulation experiments (OSSEs) demonstrate that this new TC initialization scheme leads to much improved initial MSLP, warm core, and asymmetric temperature patterns compared to those from the conventional 3DVAR scheme. Forecasts of TC intensity with the new initialization scheme are made, and the results show that the new scheme is able to predict the “observed” TC intensity change, compared to runs with the conventional 3DVAR scheme or the TCDI-only scheme. Sensitivity experiments further show that the intensity forecasts with knowledge of the initial MSLP and wind fields appear more skillful than do the cases where the initial MSLP, temperature, and humidity fields are known. The numerical experiments above demonstrate the potential usefulness of the proposed new initialization scheme in operational applications. A preliminary test of this scheme with a navy operational model shows encouraging results.

Impact of ATOVS Radiance on the Analysis and Forecasts of a Mesoscale Model over the Indian Region During the 2008 Summer Monsoon

Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance) as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July 2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture, due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model.

The AROME-France Convective-Scale Operational Model

Abstract After six years of scientific, technical developments and meteorological validation, the Application of Research to Operations at Mesoscale (AROME-France) convective-scale model became operational at Météo-France at the end of 2008. This paper presents the main characteristics of this new numerical weather prediction system: the nonhydrostatic dynamical model core, detailed moist physics, and the associated three-dimensional variational data assimilation (3D-Var) scheme. Dynamics options settings and variables are explained. The physical parameterizations are depicted as well as their mutual interactions. The scale-specific features of the 3D-Var scheme are shown. The performance of the forecast model is evaluated using objective scores and case studies that highlight its benefits and weaknesses.